通过输入是个指标对每天的涨跌进行相关预测，实现的准确率达到93%，加入交叉熵进行相关损失函数，尽量减小过拟合现象，但是在参数的最有参数选择的时候，并没有加入最优适应，需要后期进行相关的模型优化，代码如下
import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport torchfrom torch import nnfrom torch.autograd import Variabledata_csv = pd.read_csv('D:/Python/gs/data.csv')data_csv = data_csv.dropna(axis=0, how='any')data_csv = data_csv.valuesdata_x = data_csv[:, :10].astype('float32')data_y = data_csv[:, 10].astype('int32')data_x_normed = (data_x - np.min(data_x, axis=0)) / (np.max(data_x, axis=0)-np.min(data_x, axis=0))#data_x_normed=(data_x-np.mean(data_x,axis=0))/np.std(data_x,axis=0)data_x = np.array(data_x_normed)data_y = np.array(data_y)train_size = int(len(data_x) * 0.9)test_size = len(data_x) - train_sizetrain_x = data_x[:train_size]train_y = data_y[:train_size]test_x = data_x[train_size:]test_y = data_y[train_size:]train_x = train_x.reshape(-1, 1, 10)#train_y = train_y.reshape(-1, 1, 1)train_x = torch.from_numpy(train_x)train_y = torch.from_numpy(train_y)class NET(nn.Module): def __init__(self,input_size=10,hidden_size=40,output_size=2,num_layer=2): super(NET,self).__init__() self.rnn=nn.LSTM(input_size,hidden_size,num_layer) self.out=nn.Linear(hidden_size,output_size) def forward(self,x): out,_=self.rnn(x) out=self.out(out[:,-1,:]) return outnet = NET()optimizer = torch.optim.Adam(net.parameters(), lr=0.08, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)loss_func = torch.nn.CrossEntropyLoss()for epoch in range(1000): var_x=Variable(train_x).type(torch.FloatTensor) var_y=Variable(train_y).type(torch.LongTensor) out = net(var_x) loss = loss_func(out, var_y) optimizer.zero_grad() loss.backward() optimizer.step() if (epoch + 1) % 100 == 0: print('Epoch: {}, Loss: {:.5f}'.format(epoch + 1, loss.data.numpy()))test_x = test_x.reshape(-1, 1, 10)test_x = torch.from_numpy(test_x)var_data = Variable(test_x)pred_test = net(var_data)#pred_test = pred_test.view(-1).data.numpy()pred_test=torch.max(pred_test,1)[1].data.numpy().squeeze()plt.plot(pred_test, 'r', label='prediction')plt.plot(test_y, 'b', label='real')plt.legend(loc='best')plt.show()print(pred_test,'prediction number')print(test_y,'real number')j=0for i in range(test_size): if(pred_test[i] == test_y[i]): j=j+1;j=j/test_sizeprint('Identification:',j)